Heterogeneous Data Fusion for Accurate Road User Tracking: A Distributed Multi-Sensor Collaborative Approach

This work presents the design and validation of a distributed multi-sensor object tracking algorithm designed to integrate heterogeneous sensory data from multiple static acquisition stations. The primary challenge addressed is the accurate tracking of targets in complex urban environments, where occlusions and the dynamic nature of traffic frequently hinder detection and tracking efforts. This challenge is particularly relevant in multimodal exchange areas, where vehicular traffic merges with heavy pedestrian and bicycle flow. We also address the scenario of delayed detection, which can easily occur when data from multiple stations are combined or when intensive data processing is performed. Our algorithm ensures high coverage and accuracy by maintaining dual Extended Kalman Filter states for each object, thus allowing for the assimilation of delayed detections and preserving optimal filter estimates at all times. The results of the proposed pipeline, tested using a digital twin of the Milano Bovisa Campus, demonstrate its efficacy, achieving high tracking precision across various scenarios and sensor combinations. Moreover, the results highlight the advantages of a distributed multi-sensor acquisition system compared to a single central station.